R Tutorial

An introduction to R

Introduction

This tutorial is will introduce the reader to , a free, open-source statistical computing environment often used with RStudio, a integrated development environment for .

R Project Logo

Download

Download at https://www.r-project.org/

Download RStudio at https://rstudio.com/products/rstudio/download/

Calculator

can be used as a super awesome calculator

# 5 + 3 = 8
5 + 3

## [1] 8

# 24 / (1 + 2) = 8
24 / (1 + 2)

## [1] 8

# 2 * 2 * 2 = 8
2^3

## [1] 8

# 8 * 8 = 64
sqrt(64)

## [1] 8

# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000)

## [1] 8

Functions

has many useful built in functions

1:10

##  [1]  1  2  3  4  5  6  7  8  9 10

as.character(1:10)

##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"

rep(1:2, times = 5)

##  [1] 1 2 1 2 1 2 1 2 1 2

rep(1:5, times = 2)

##  [1] 1 2 3 4 5 1 2 3 4 5

rep(1:5, each = 2)

##  [1] 1 1 2 2 3 3 4 4 5 5

rep(1:5, length.out = 7)

## [1] 1 2 3 4 5 1 2

seq(5, 50, by = 5)

##  [1]  5 10 15 20 25 30 35 40 45 50

seq(5, 50, length.out = 5)

## [1]  5.00 16.25 27.50 38.75 50.00

paste(1:10, 20:30, sep = "-")

##  [1] "1-20"  "2-21"  "3-22"  "4-23"  "5-24"  "6-25"  "7-26"  "8-27"  "9-28"  "10-29" "1-30"

paste(1:10, collapse = "-")

## [1] "1-2-3-4-5-6-7-8-9-10"

paste0("x", 1:10)

##  [1] "x1"  "x2"  "x3"  "x4"  "x5"  "x6"  "x7"  "x8"  "x9"  "x10"

min(1:10)

## [1] 1

max(1:10)

## [1] 10

range(1:10)

## [1]  1 10

mean(1:10)

## [1] 5.5

sd(1:10)

## [1] 3.02765

Custom Functions

Users can also create their own functions

customFunction1 <- function(x, y) {
  z <- 100 * x / (x + y)
  paste(z, "%")
}
customFunction1(x = 10, y = 90)

## [1] "10 %"

customFunction2 <- function(x) {
  mymin <- mean(x - sd(x))
  mymax <- mean(x) + sd(x)
  print(paste("Min =", mymin))
  print(paste("Max =", mymax))
}
customFunction2(x = 1:10)

## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"

`for` loops and `if` `else` statements

xx <- NULL #creates and empty object
for(i in 1:10) {
  xx[i] <- i*3
}
xx

##  [1]  3  6  9 12 15 18 21 24 27 30

xx %% 2 #gives the remainder when divided by 2

##  [1] 1 0 1 0 1 0 1 0 1 0

for(i in 1:length(xx)) {
  if((xx[i] %% 2) == 0) {
    print(paste(xx[i],"is Even"))
  } else { 
      print(paste(xx[i],"is Odd")) 
    }
}

## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"

# or
ifelse(xx %% 2 == 0, "Even", "Odd")

##  [1] "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even"

paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))

##  [1] "3 is Odd"   "6 is Even"  "9 is Odd"   "12 is Even" "15 is Odd"  "18 is Even" "21 is Odd" 
##  [8] "24 is Even" "27 is Odd"  "30 is Even"

Objects

Information can be stored in user defined objects, in multiple forms:

c(): a string of values
matrix(): a two dimensional matrix in one format
data.frame(): a two dimensional matrix where each column can be a different format
list():

A string…

xc <- 1:10
xc

##  [1]  1  2  3  4  5  6  7  8  9 10

xc <- c(1,2,3,4,5,6,7,8,9,10)
xc

##  [1]  1  2  3  4  5  6  7  8  9 10

A matrix…

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1    2    3    4    5    6    7    8    9    10
##  [2,]   11   12   13   14   15   16   17   18   19    20
##  [3,]   21   22   23   24   25   26   27   28   29    30
##  [4,]   31   32   33   34   35   36   37   38   39    40
##  [5,]   41   42   43   44   45   46   47   48   49    50
##  [6,]   51   52   53   54   55   56   57   58   59    60
##  [7,]   61   62   63   64   65   66   67   68   69    70
##  [8,]   71   72   73   74   75   76   77   78   79    80
##  [9,]   81   82   83   84   85   86   87   88   89    90
## [10,]   91   92   93   94   95   96   97   98   99   100

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

A data frame…

xd <- data.frame(
  x1 = c("aa","bb","cc","dd","ee",
         "ff","gg","hh","ii","jj"),
  x2 = 1:10,
  x3 = c(1,1,1,1,1,2,2,2,3,3),
  x4 = rep(c(1,2), times = 5),
  x5 = rep(1:5, times = 2),
  x6 = rep(1:5, each = 2),
  x7 = seq(5, 50, by = 5),
  x8 = log10(1:10),
  x9 = (1:10)^3,
  x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd

##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

A list…

xl <- list(xc, xm, xd)
xl[[1]]

##  [1]  1  2  3  4  5  6  7  8  9 10

xl[[2]]

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

xl[[3]]

##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

Selecting Data

xc[5] # 5th element in xc

## [1] 5

xd$x3[5] # 5th element in col "x3"

## [1] 1

xd[5,"x3"] # row 5, col "x3"

## [1] 1

xd$x3 # all of col "x3"

##  [1] 1 1 1 1 1 2 2 2 3 3

xd[,"x3"] # all rows, col "x3"

##  [1] 1 1 1 1 1 2 2 2 3 3

xd[3,] # row 3, all cols

##   x1 x2 x3 x4 x5 x6 x7        x8 x9  x10
## 3 cc  3  1  1  3  2 15 0.4771213 27 TRUE

xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"

##   x4 x5
## 2  2  2
## 4  2  4

xl[[3]]$x1 # 3rd object in the list, col "x1

##  [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"

regexpr

xx <- data.frame(Name = c("Item 1 (detail 1)",
                          "Item 20 (detail 20)",
                          "Item 300 (detail 300)"),
                 Item = NA,
                 Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx

##                    Name     Item     Detail
## 1     Item 1 (detail 1)   Item 1   detail 1
## 2   Item 20 (detail 20)  Item 20  detail 20
## 3 Item 300 (detail 300) Item 300 detail 300

Data Formats

Data can also be saved in many formats:

numeric
integer
character
factor
logical

xd$x3 <- as.character(xd$x3)
xd$x3

##  [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"

xd$x3 <- as.numeric(xd$x3)
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3

xd$x3 <- as.factor(xd$x3)
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3

xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1

xd$x10

##  [1]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE

as.numeric(xd$x10) # TRUE = 1, FALSE = 0

##  [1] 1 1 1 0 0 1 1 0 0 0

sum(xd$x10)

## [1] 5

Internal structure of an object can be checked with str()

str(xc) # c()

##  num [1:10] 1 2 3 4 5 6 7 8 9 10

str(xm) # matrix()

##  int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...

str(xd) # data.frame()

## 'data.frame':    10 obs. of  10 variables:
##  $ x1 : chr  "aa" "bb" "cc" "dd" ...
##  $ x2 : int  1 2 3 4 5 6 7 8 9 10
##  $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
##  $ x4 : num  1 2 1 2 1 2 1 2 1 2
##  $ x5 : int  1 2 3 4 5 1 2 3 4 5
##  $ x6 : int  1 1 2 2 3 3 4 4 5 5
##  $ x7 : num  5 10 15 20 25 30 35 40 45 50
##  $ x8 : num  0 0.301 0.477 0.602 0.699 ...
##  $ x9 : num  1 8 27 64 125 216 343 512 729 1000
##  $ x10: logi  TRUE TRUE TRUE FALSE FALSE TRUE ...

str(xl) # list()

## List of 3
##  $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
##  $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
##  $ :'data.frame':    10 obs. of  10 variables:
##   ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
##   ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
##   ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
##   ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
##   ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
##   ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
##   ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
##   ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
##   ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
##   ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...

Packages

Additional libraries can be installed and loaded for use.

install.packages("scales")

library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx

##    Values  Rescaled
## 1       1  1.000000
## 2       2  4.222222
## 3       3  7.444444
## 4       4 10.666667
## 5       5 13.888889
## 6       6 17.111111
## 7       7 20.333333
## 8       8 23.555556
## 9       9 26.777778
## 10     10 30.000000

libraries can also be used without having to load them

scales::rescale(1:10, to = c(1,30))

##  [1]  1.000000  4.222222  7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778
## [10] 30.000000

Data Wrangling

R for Data Science - https://r4ds.had.co.nz/

xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
                 Data1 = 1:10, 
                 Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx

##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      X     6    60       66     6000
## 7      X     7    70       77     7000
## 8      X     8    80       88     8000
## 9      Y     9    90       99     9000
## 10     Y    10   100      110    10000

xx$Data1 < 5 # which are less than 5

##  [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE

xx[xx$Data1 < 5,]

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx[xx$Group == "X", c("Group","Data2","NewData1")]

##   Group Data2 NewData1
## 1     X    10       11
## 2     X    20       22
## 6     X    60       66
## 7     X    70       77
## 8     X    80       88

Data wrangling with tidyverse and pipes (%>%)

library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
  mutate(Data1 = 1:10, 
         Data2 = seq(10, 100, by = 10),
         NewData1 = Data1 + Data2,
         NewData2 = Data1 * 1000)
xx

##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      Y     6    60       66     6000
## 7      Y     7    70       77     7000
## 8      X     8    80       88     8000
## 9      X     9    90       99     9000
## 10     X    10   100      110    10000

filter(xx, Data1 < 5)

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx %>% filter(Data1 < 5)

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx %>% filter(Group == "X") %>% 
  select(Group, NewColName=Data2, NewData1)

##   Group NewColName NewData1
## 1     X         10       11
## 2     X         20       22
## 3     X         80       88
## 4     X         90       99
## 5     X        100      110

xs <- xx %>% 
  group_by(Group) %>% 
  summarise(Data2_mean = mean(Data2),
            Data2_sd = sd(Data2),
            NewData2_mean = mean(NewData2),
            NewData2_sd = sd(NewData2))
xs

## # A tibble: 2 × 5
##   Group Data2_mean Data2_sd NewData2_mean NewData2_sd
##   <chr>      <dbl>    <dbl>         <dbl>       <dbl>
## 1 X             60     41.8          6000       4183.
## 2 Y             50     15.8          5000       1581.

xx %>% left_join(xs, by = "Group")

##    Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1      X     1    10       11     1000         60 41.83300          6000    4183.300
## 2      X     2    20       22     2000         60 41.83300          6000    4183.300
## 3      Y     3    30       33     3000         50 15.81139          5000    1581.139
## 4      Y     4    40       44     4000         50 15.81139          5000    1581.139
## 5      Y     5    50       55     5000         50 15.81139          5000    1581.139
## 6      Y     6    60       66     6000         50 15.81139          5000    1581.139
## 7      Y     7    70       77     7000         50 15.81139          5000    1581.139
## 8      X     8    80       88     8000         60 41.83300          6000    4183.300
## 9      X     9    90       99     9000         60 41.83300          6000    4183.300
## 10     X    10   100      110    10000         60 41.83300          6000    4183.300

Read/Write data

xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)

For excel sheets, the package readxl can be used to read in sheets of data.

library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")

Tidy Data

Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html

Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html

yy <- xx %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = round(mean(DTF),1)) %>% 
  arrange(Location)
yy

## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          Location            Mean_DTF
##   <chr>         <chr>                  <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh     86.7
## 2 ILL 618 AGL   Jessore, Bangladesh     79.3
## 3 Laird AGL     Jessore, Bangladesh     76.8
## 4 CDC Maxim AGL Metaponto, Italy       134. 
## 5 ILL 618 AGL   Metaponto, Italy       138. 
## 6 Laird AGL     Metaponto, Italy       137. 
## 7 CDC Maxim AGL Saskatoon, Canada       52.5
## 8 ILL 618 AGL   Saskatoon, Canada       47  
## 9 Laird AGL     Saskatoon, Canada       56.8

yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy

## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
##   <chr>                         <dbl>              <dbl>               <dbl>
## 1 CDC Maxim AGL                  86.7               134.                52.5
## 2 ILL 618 AGL                    79.3               138.                47  
## 3 Laird AGL                      76.8               137.                56.8

yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy

## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          TraitName           Value
##   <chr>         <chr>               <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh  86.7
## 2 ILL 618 AGL   Jessore, Bangladesh  79.3
## 3 Laird AGL     Jessore, Bangladesh  76.8
## 4 CDC Maxim AGL Metaponto, Italy    134. 
## 5 ILL 618 AGL   Metaponto, Italy    138. 
## 6 Laird AGL     Metaponto, Italy    137. 
## 7 CDC Maxim AGL Saskatoon, Canada    52.5
## 8 ILL 618 AGL   Saskatoon, Canada    47  
## 9 Laird AGL     Saskatoon, Canada    56.8

yy <- yy %>% spread(key = Name, value = Value)
yy

## # A tibble: 3 × 4
##   TraitName           `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
##   <chr>                         <dbl>         <dbl>       <dbl>
## 1 Jessore, Bangladesh            86.7          79.3        76.8
## 2 Metaponto, Italy              134.          138.        137. 
## 3 Saskatoon, Canada              52.5          47          56.8

Base Plotting

We will start with some basic plotting using the base function plot()

Tutorial 1 - http://www.sthda.com/english/wiki/r-base-graphs

Tutorial 2 - https://bookdown.org/rdpeng/exdata/the-base-plotting-system-1.html

# A basic scatter plot
plot(x = xd$x8, y = xd$x9)

# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)

plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)

# Adjust plot type 
plot(x = xd$x8, y = xd$x9, type = "line")

# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)

# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")

Now lets create some random and normally distributed data to make some more complicated plots

# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru

##   [1] 52.210933 77.089070 22.223178 47.980682 11.863842 34.227134 22.347987 26.120469 41.259464
##  [10] 67.244491 37.436665 86.021633 88.522717 88.457542 53.152679 71.669342  3.795825 56.131983
##  [19] 34.953257 48.620229 99.855446 95.317950  1.598255 21.722332 45.889581 50.132789 37.509386
##  [28]  6.590404 41.136983 94.867255 42.976475 39.459419 27.648349 56.662095 95.546371 59.642545
##  [37] 16.052295 95.001301 54.176337 93.884122 21.619024 24.032549 30.319671 49.392522 20.345725
##  [46] 38.307453 15.234455 13.509994 74.940776 56.084477 34.342150 15.845061 31.146525  2.555214
##  [55] 20.032040 60.138680 95.213598 65.909653 92.625947  6.461340 48.923772 91.526284 15.074397
##  [64] 20.744736 18.766871 62.041615 20.103348 45.091412 63.889581  6.100128 14.377089 61.690660
##  [73] 44.491739 66.291703 62.575441 31.263630 49.337508 60.914250 22.353060 17.639389 60.047044
##  [82] 48.404878 34.798999 83.647268 10.498487 20.926845 62.337410 92.023601 71.516589 71.711266
##  [91] 79.860228 21.732220 95.367822 94.709392 78.705871 95.394084 36.110328 85.528372 13.837273
## [100] 85.837386

plot(x = ru)

order(ru)

##   [1]  23  54  17  70  60  28  85   5  48  99  71  63  47  52  37  80  65  55  67  45  64  86  41
##  [24]  24  92   3   7  79  42   8  33  43  53  76   6  51  83  19  97  11  27  46  32  29   9  31
##  [47]  73  68  25   4  82  20  61  77  44  26   1  15  39  50  18  34  36  81  56  78  72  66  87
##  [70]  75  69  58  74  10  89  16  90  49   2  95  91  84  98 100  12  14  13  62  88  59  40  94
##  [93]  30  38  57  22  93  96  35  21

ru<- ru[order(ru)]
ru

##   [1]  1.598255  2.555214  3.795825  6.100128  6.461340  6.590404 10.498487 11.863842 13.509994
##  [10] 13.837273 14.377089 15.074397 15.234455 15.845061 16.052295 17.639389 18.766871 20.032040
##  [19] 20.103348 20.345725 20.744736 20.926845 21.619024 21.722332 21.732220 22.223178 22.347987
##  [28] 22.353060 24.032549 26.120469 27.648349 30.319671 31.146525 31.263630 34.227134 34.342150
##  [37] 34.798999 34.953257 36.110328 37.436665 37.509386 38.307453 39.459419 41.136983 41.259464
##  [46] 42.976475 44.491739 45.091412 45.889581 47.980682 48.404878 48.620229 48.923772 49.337508
##  [55] 49.392522 50.132789 52.210933 53.152679 54.176337 56.084477 56.131983 56.662095 59.642545
##  [64] 60.047044 60.138680 60.914250 61.690660 62.041615 62.337410 62.575441 63.889581 65.909653
##  [73] 66.291703 67.244491 71.516589 71.669342 71.711266 74.940776 77.089070 78.705871 79.860228
##  [82] 83.647268 85.528372 85.837386 86.021633 88.457542 88.522717 91.526284 92.023601 92.625947
##  [91] 93.884122 94.709392 94.867255 95.001301 95.213598 95.317950 95.367822 95.394084 95.546371
## [100] 99.855446

plot(x = ru)

# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd

##   [1] 42.97112 31.39466 52.52831 62.37741 48.70014 48.50260 46.95884 50.34109 50.89171 34.56138
##  [11] 65.82957 41.70603 46.05539 45.33871 43.24662 51.42495 53.80572 41.18225 54.33411 50.59074
##  [21] 54.58000 38.41469 49.97863 58.86807 57.06030 47.12699 42.97793 48.25317 50.61250 50.11703
##  [31] 49.56403 35.59563 40.36258 49.14205 46.26921 39.20003 56.66852 44.37822 56.02320 54.78272
##  [41] 53.89745 59.22403 43.56070 51.44920 62.07752 34.84659 51.46753 71.57826 50.10523 49.66146
##  [51] 45.07747 53.49664 37.30432 48.90779 41.76741 52.57373 45.32352 50.16299 39.70838 50.45612
##  [61] 41.77366 42.81246 49.31118 49.41905 41.44008 59.68821 52.84081 53.25198 50.54449 48.90027
##  [71] 54.47799 56.88422 36.42945 21.06417 47.87015 51.30928 55.91533 28.68912 50.48769 41.72218
##  [81] 39.00388 48.89316 62.22051 44.52278 43.14925 56.40151 53.38645 43.20832 60.14274 46.37953
##  [91] 40.47696 33.99647 68.79216 46.77218 47.40602 50.61993 38.02137 50.50562 25.80663 50.02401

nd <- nd[order(nd)]
nd

##   [1] 21.06417 25.80663 28.68912 31.39466 33.99647 34.56138 34.84659 35.59563 36.42945 37.30432
##  [11] 38.02137 38.41469 39.00388 39.20003 39.70838 40.36258 40.47696 41.18225 41.44008 41.70603
##  [21] 41.72218 41.76741 41.77366 42.81246 42.97112 42.97793 43.14925 43.20832 43.24662 43.56070
##  [31] 44.37822 44.52278 45.07747 45.32352 45.33871 46.05539 46.26921 46.37953 46.77218 46.95884
##  [41] 47.12699 47.40602 47.87015 48.25317 48.50260 48.70014 48.89316 48.90027 48.90779 49.14205
##  [51] 49.31118 49.41905 49.56403 49.66146 49.97863 50.02401 50.10523 50.11703 50.16299 50.34109
##  [61] 50.45612 50.48769 50.50562 50.54449 50.59074 50.61250 50.61993 50.89171 51.30928 51.42495
##  [71] 51.44920 51.46753 52.52831 52.57373 52.84081 53.25198 53.38645 53.49664 53.80572 53.89745
##  [81] 54.33411 54.47799 54.58000 54.78272 55.91533 56.02320 56.40151 56.66852 56.88422 57.06030
##  [91] 58.86807 59.22403 59.68821 60.14274 62.07752 62.22051 62.37741 65.82957 68.79216 71.57826

plot(x = nd)

hist(x = nd)

hist(nd, breaks = 20, col = "darkgreen")

plot(x = density(nd))

boxplot(x = nd)

boxplot(x = nd, horizontal = T)

ggplot2

Lets be honest, the base plots are ugly! The ggplot2 package gives the user to create a better, more visually appealing plots. Additional packages such as ggbeeswarm and ggrepel also contain useful functions to add to the functionality of ggplot2.

ggplot2 - https://ggplot2.tidyverse.org/

Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html

Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/

The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html

library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()

mp + geom_point(aes(color = x3, shape = x3), size = 4)

mp + geom_line(size = 2)

mp + geom_line(aes(color = x3), size = 2)

mp + geom_smooth(method = "loess")

mp + geom_smooth(method = "lm")

xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
                          rnorm(50, mean = 60, sd = 5)),
                 group = factor(rep(1:2, each = 50)),
                 label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")

mp + geom_histogram(color = "black", position = "dodge")

mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1

mp + geom_density(alpha = 0.5)

mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")

mp + geom_boxplot() + geom_point()

mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")

library(ggbeeswarm)
mp + geom_quasirandom()

mp + geom_quasirandom(aes(shape = group))

mp2 <- mp + geom_violin() + 
  geom_boxplot(width = 0.1, fill = "white") +
  geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)

library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
          common.legend = T, legend = "bottom")

Statistics

Handbook of Biological Statistics - http://biostathandbook.com/

R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html

# Prep data
lev_Loc  <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
  mutate(Location = factor(Location, levels = lev_Loc),
         Name = factor(Name, levels = lev_Name))
xx <- dd %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)

## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
##   <fct>                       <dbl>                 <dbl>              <dbl>
## 1 ILL 618 AGL                  47                    79.3               138.
## 2 CDC Maxim AGL                52.5                  86.7               134.
## 3 Laird AGL                    56.8                  76.8               137.

# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
  geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF, 
                      color = Name, group = Name, shape = Name)) +
  geom_point(size = 2.5, alpha = 0.7) + 
  geom_line(size = 1, alpha = 0.7) +
  theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")

From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.

summary(aov(DTF ~ Name * Location, data = dd))

##               Df Sum Sq Mean Sq  F value   Pr(>F)    
## Name           2     88      44    3.476   0.0395 *  
## Location       2  65863   32931 2598.336  < 2e-16 ***
## Name:Location  4    560     140   11.044 2.52e-06 ***
## Residuals     45    570      13                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.

If we only have two things to compare, we could do a t-test.

xx <- dd %>% 
  filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
  spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)

## 
##  Welch Two Sample t-test
## 
## data:  xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -32.18265 -25.48402
## sample estimates:
## mean of x mean of y 
##  52.11111  80.94444

DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.

xx <- dd %>% 
  filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
         Location == "Metaponto, Italy") %>%
  spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)

## 
##  Welch Two Sample t-test
## 
## data:  xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.059739  7.059739
## sample estimates:
## mean of x mean of y 
##  137.8333  136.8333

DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.

pch Plot

xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
                 y = rep(5:1, each = 6, length.out = 26),
                 pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
  geom_point(color = "darkred", fill = "darkblue", size = 5) +
  geom_text(aes(label = pch), nudge_x = -0.25) +
  scale_shape_manual(values = xx$pch) +
  scale_x_continuous(breaks = 6:1) +
  scale_y_continuous(breaks = 6:1) +
  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5),
        axis.text = element_blank(),
        axis.ticks = element_blank()) +
  labs(title = "Plot symbols in R (pch)",
       subtitle = "color = \"darkred\", fill = \"darkblue\"",
       x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")

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